def resolve(self, y0):
"""
resolve the problem locally on one chunk
returns local solution and Khi2
"""
Z = len(self.axeZ)
N = len(self._axeMframe)
if FAST:
dec.AX = self._axeMframe
scale = y0.max()
y = y0/scale
noise, offset = findnoise_offset(y)
# normK=np.sqrt(Z),
xrec_PDS, refspec = PDS.pds_vf(self.K1, self.Kt1, Z*N, y-offset, eta=noise*np.sqrt(N), normK=np.sqrt(Z), lamda=self.lamda, nbiter=self.nbiter, ref=None, Khi2=self.computeKhi2, show=False)
return xrec_PDS.reshape(Z,N)*scale, refspec
def main():
log = ECC.setup_logging(debug=False)
(pds, families, members) = PDSChurch.load_families_and_members(
filename='pdschurch.sqlite3', log=log)
ministries = PDS.read_table(pds,
'MinType_DB',
'MinDescRec',
columns=['Description'],
log=log)
flipped = dict()
for key, item in ministries.items():
flipped[item['Description']] = item
for key in sorted(flipped):
print(key)
def _load_members(pds, columns=None, active_only=True, log=None):
db_num = _get_db_num()
if not columns:
columns = list()
columns.append('Name')
columns.append('FamRecNum')
columns.append('DateOfBirth')
columns.append('MonthOfBirth')
columns.append('DayOfBirth')
columns.append('YearOfBirth')
columns.append('Gender')
columns.append('MaritalStatusRec')
columns.append('MemberType')
columns.append('PictureFile')
columns.append('Location')
columns.append('LanguageRec')
columns.append('EthnicDescRec')
columns.append('User3DescRec') # Skills
columns.append('User4DescRec') # Occupation
columns.append('Deceased')
columns.append('PDSInactive{num}'.format(num=db_num))
where = ('Mem_DB.CensusMember{db_num}=1'.format(db_num=db_num))
if active_only:
where += (' AND '
'Mem_DB.deceased=0 AND '
'(Mem_DB.PDSInactive{db_num}=0 OR '
'Mem_DB.PDSInactive{db_num} is null)'.format(db_num=db_num))
members = PDS.read_table(pds,
'Mem_DB',
'MemRecNum',
columns=columns,
log=log,
where=where)
for m in members.values():
_normalize_boolean(m, src='Deceased')
_normalize_boolean(m, src=f'PDSInactive{db_num}', dest="Inactive")
_normalize_filename(m, src='PictureFile')
m['date_of_birth'] = _normalize_date(m['DateOfBirth'], sentinel=False)
return members
def resolve(self, y0):
"""
resolve the problem locally on one chunk
returns local solution and Khi2
"""
Z1 = len(self.axeZ1)
N1 = len(self.axeM1)
Z2 = len(self.axeZ2)
N2 = len(self.axeM2)
if FAST:
dec.AX1 = self.axeM1
dec.AX2 = self.axeM2
scale = y0.max()
y = y0/scale
self.noise, self.offset = findnoise_offset(y.ravel())
# normK=np.sqrt(Z),
xrec_PDS, refspec = PDS.pds_vf(self.K1, self.Kt1, Z1*Z2*N1*N2, y.ravel()-self.offset, eta=self.noisefactor*self.noise*np.sqrt(N1*N2),
normK=np.sqrt(Z1*Z2), lamda=self.lamda, nbiter=self.nbiter, ref=None, Khi2=self.computeKhi2, show=False)
return xrec_PDS.reshape(Z1,Z2,N1,N2)*scale, refspec
def _load_families(pds, columns=None, active_only=True, log=None):
db_num = _get_db_num()
if not columns:
columns = list()
columns.append('Name')
columns.append('MailingName')
columns.append('ParKey')
columns.append('StreetAddress1')
columns.append('StreetAddress2')
columns.append('StreetCityRec')
columns.append('StreetZip')
columns.append('StatDescRec')
columns.append('PictureFile')
columns.append('EnvelopeUser')
columns.append('Visitor')
columns.append('SendNoMail')
columns.append('PDSInactive{num}'.format(num=db_num))
where = ('Fam_DB.CensusFamily{db_num}=1'.format(db_num=db_num))
if active_only:
where += (' AND '
'(Fam_DB.PDSInactive{db_num}=0 OR '
'FAM_DB.PDSInactive{db_num} is null)'.format(db_num=db_num))
families = PDS.read_table(pds,
'Fam_DB',
'FamRecNum',
columns=columns,
log=log,
where=where)
for f in families.values():
_normalize_boolean(f, src=f'PDSInactive{db_num}', dest="Inactive")
_normalize_boolean(f, src='SendNoMail')
_normalize_boolean(f, src='EnvelopeUser')
_normalize_filename(f, src='PictureFile')
return families
def load_families_and_members(filename=None,
pds=None,
active_only=True,
parishioners_only=True,
log=None):
if pds and filename:
raise Exception(
"Cannot supply both filename *and* PDS SQLite3 cursor -- only supply one or the other"
)
if filename:
pds = PDS.connect(filename)
city_states = PDS.read_table(pds,
'City_DB',
'CityRec',
columns=['CityState'],
log=log)
statuses = PDS.read_table(pds,
'StatusType_DB',
'StatusDescRec',
columns=['Description', 'Active'],
log=log)
ministries = PDS.read_table(pds,
'MinType_DB',
'MinDescRec',
columns=['Description'],
log=log)
talents = PDS.read_table(pds,
'TalType_DB',
'TalDescRec',
columns=['Description'],
log=log)
birth_places = PDS.read_table(pds,
'Ask_DB',
'AskRecNum',
columns=['AskMemNum', 'BirthPlace'],
log=log)
date_places = PDS.read_table(pds,
'DatePlace_DB',
'DatePlaceRecNum',
log=log)
date_types = PDS.read_table(pds,
'DateType_DB',
'DescRec',
columns=['Description'],
log=log)
phone_types = PDS.read_table(pds,
'PhoneTyp_DB',
'PhoneTypeRec',
columns=['Description'],
log=log)
req_types = PDS.read_table(pds,
'ReqType_DB',
'ReqDescRec',
columns=['Description', 'Expires'],
log=log)
emails = PDS.read_table(
pds,
'MemEMail_DB',
'EMailRec',
columns=['MemRecNum', 'EMailAddress', 'EMailOverMail', 'FamEmail'],
log=log)
languages = PDS.read_table(pds,
'LangType_DB',
'LanguageRec',
columns=['Description'],
log=log)
mem_phones = PDS.read_table(
pds,
'MemPhone_DB',
'PhoneRec',
columns=['Rec', 'Number', 'PhoneTypeRec', 'Unlisted'],
log=log)
mem_keyword_types = PDS.read_table(pds,
'MemKWType_DB',
'DescRec',
columns=['Description'],
log=log)
mem_keywords = PDS.read_table(pds,
'MemKW_DB',
'MemKWRecNum',
columns=['MemRecNum', 'DescRec'],
log=log)
mem_ministries = PDS.read_table(
pds,
'MemMin_DB',
'MemKWRecNum',
columns=['MinDescRec', 'MemRecNum', 'StatusDescRec'],
log=log)
mem_talents = PDS.read_table(
pds,
'MemTal_DB',
'MemKWRecNum',
columns=['TalDescRec', 'MemRecNum', 'StatusDescRec'],
log=log)
mem_dates = PDS.read_table(pds,
'MemDates_DB',
'MemDateRecNum',
columns=['MemRecNum', 'Date', 'DescRec'],
log=log)
mem_ethnics = PDS.read_table(pds,
'EthType_DB',
'EthnicDescRec',
columns=['Description'],
log=log)
mem_3kw = PDS.read_table(pds,
'User3KW_DB',
'User3DescRec',
columns=['Description'],
log=log)
mem_4kw = PDS.read_table(pds,
'User4KW_DB',
'User4DescRec',
columns=['Description'],
log=log)
mem_reqs = PDS.read_table(pds,
'MemReq_DB',
'MemReqRecNum',
columns=[
'MemRecNum', 'ReqDescRec', 'ReqDate',
'ReqResult', 'ReqNote', 'ExpirationDate'
])
relationship_types = PDS.read_table(pds,
'RelType_DB',
'RelDescRec',
columns=['Description'],
log=log)
marital_statuses = PDS.read_table(pds,
'MemStatType_DB',
'MaritalStatusRec',
columns=['Description'],
log=log)
fam_keyword_types = PDS.read_table(pds,
'FamKWType_DB',
'DescRec',
columns=['Description'],
log=log)
fam_keywords = PDS.read_table(pds,
'FamKW_DB',
'FamKWRecNum',
columns=['FamRecNum', 'DescRec'],
log=log)
fam_status_types = PDS.read_table(pds,
'FamStatType_DB',
'StatDescRec',
columns=['Description'],
log=log)
fam_phones = PDS.read_table(
pds,
'FamPhone_DB',
'PhoneRec',
columns=['Rec', 'Number', 'PhoneTypeRec', 'Unlisted'],
log=log)
# Descriptions of each fund
funds = PDS.read_table(pds,
'FundSetup_DB',
'SetupRecNum',
columns=['FundNumber', 'FundKey', 'FundName'],
log=log)
# Each fund also has one or more time periods associated with it
fund_periods = PDS.read_table(pds,
'FundPeriod_DB',
'FundPeriodRecNum',
columns=[
'SetupRecNum', 'FundNumber', 'FundYear',
'FundStart', 'FundEnd'
],
log=log)
# When a Family contributes, each contribution is assocaited with
# a "funding activity"
fund_activities = PDS.read_table(pds,
'FundAct_DB',
'ActRecNum',
columns=[
'FundRecNum', 'GroupName', 'Activity',
'Function', 'GroupOrder', 'pdsorder'
],
log=log)
# Families' activities with relation to the established funds (there is one
# entry for each family for each fund to which that family has contributed).
fam_funds = PDS.read_table(pds,
'FamFund_DB',
'FDRecNum',
columns=[
'FDFamRec', 'FDYear', 'FDFund', 'FDOrder',
'MemRecNum', 'Comment'
],
log=log)
# Pledging information from the family
fam_fund_rates = PDS.read_table(pds,
'FamFundRate_DB',
'RateRecNum',
columns=[
'FundRecNum', 'FDStartDate',
'FDEndDate', 'FDRate', 'FDRateAdj',
'FDNumber', 'FDPeriod', 'FDTotal',
'Batch', 'BatchDate'
])
# A listing of each individual contribution from each family,
# cross-referenced to fam_funds.
fam_fund_history = PDS.read_table(pds,
'FamFundHist_DB',
'FERecNum',
columns=[
'FEDate', 'ActRecNum', 'FEFundRec',
'FEFamRec', 'FEAmt', 'FEBatch',
'MemRecNum', 'FEChk', 'FEComment'
],
log=log)
member_types = _find_member_types()
mdtid = _find_member_marriage_date_type(date_types)
_make_emails_lower_case(emails)
families = _load_families(pds=pds, active_only=active_only, log=log)
members = _load_members(pds=pds, active_only=active_only, log=log)
_link_families_members(families, members)
if parishioners_only:
_delete_non_parishioners(families, members)
_link_family_emails(families, emails)
_link_family_city_states(families, city_states)
_link_family_statuses(families, fam_status_types)
_link_family_phones(families, fam_phones, phone_types)
_link_family_keywords(families, fam_keyword_types, fam_keywords)
_parse_member_names(members)
_link_member_types(members, member_types)
_link_member_emails(members, emails)
_link_member_phones(members, mem_phones, phone_types)
_link_member_keywords(members, mem_keyword_types, mem_keywords)
_link_member_birth_places(members, birth_places)
_link_member_ministries(members, ministries, mem_ministries, statuses)
_link_member_talents(members, talents, mem_talents, statuses)
_link_member_marriage_dates(members, mem_dates, mdtid)
_link_member_requirements(members, mem_reqs, req_types)
_link_member_id(members, 'MaritalStatusRec', 'marital_status',
marital_statuses)
_link_member_id(members, 'LanguageRec', 'language', languages)
_link_member_id(members, 'EthnicDescRec', 'ethnic', mem_ethnics)
_link_member_id(members, 'User3DescRec', 'skills', mem_3kw)
_link_member_id(members, 'User4DescRec', 'occupation', mem_4kw)
_link_family_funds(funds, fund_periods, fund_activities, families,
fam_funds, fam_fund_rates, fam_fund_history, log)
# Compute family HoH+Spouse salutations
_compute_family_hoh_and_spouse_salutations(families, log)
return pds, families, members
plt.plot(x, y, 'b', label='样本' + str(j) + '(Slaver)')
plt.legend(prop=zhfont1)
plt.xlabel("波长 (nm)", fontproperties=zhfont1)
plt.ylabel("吸光度 (AU)", fontproperties=zhfont1)
plt.title("两台仪器3组玉米样本光谱对比", fontproperties=zhfont1)
plt.show()
#import numpy as np
import PDS
import Shenks
# 取前 3 组样本
Rms = m5nbsArray[0:3, :]
Rss = mp5nbsArray[0:3, :]
pds = PDS.PDS(Rms, Rss)
# 窗口大小选择
win = 1
k = 1
pds.model(win, k)
Rssun = mp5nbsArray[0:3, :]
Rmsun = m5nbsArray[0:3, :][:, win:Rssun.shape[1] - win]
Rssyuce = pds.predict(Rssun)
# 舍弃窗口两端
xwin = x[win:Rssun.shape[1] - win]
#y1 = averSpec(Rmsyuce) # 预测值
#plt.plot(xwin, y1,'r')
# y = Rmsun[j, :] - 0.5 * j #真实值
# plt.plot(xwin, y, 'b')